Category Archives: BayesFactor

BayesFactor updated to version 0.9.11-1

The BayesFactor package has been updated to version 0.9.11-1. The changes are:

  CHANGES IN BayesFactor VERSION 0.9.11-1

  * Fixed memory bug causing importance sampling to fail.

  CHANGES IN BayesFactor VERSION 0.9.11

  * Added support for prior/posterior odds and probabilities. See the new vignette for details.
  * Added approximation for t test in case of large t
  * Made some error messages clearer
  * Use callbacks at least once in all cases
  * Fix bug preventing continuous interactions from showing in regression Gibbs sampler
  * Removed unexported function oneWayAOV.Gibbs(), and related C functions, due to redundancy
  * gMap from model.matrix is now 0-indexed vector (for compatibility with C functions)
  * substantial changes to backend, to Rcpp and RcppEigen for speed
  * removed redundant struc argument from nWayAOV (use gMap instead)

At the APS Observer: a profile of JASP

The APS Observer has just published a profile of JASP, a graphical user interface designed to make statistics easier. It includes Bayesian procedures by means of the R and the BayesFactor package. From the article:

 JASP distinguishes itself from SPSS by being as simple, intuitive, and approachable as possible, and by making accessible some of the latest developments in Bayesian analyses. At time of writing, JASP version 0.6 implements the following analysis tools in both their classical and Bayesian manifestations:
  • Descriptive statistics
  • t tests
  • Independent samples ANOVA
  • Repeated measures ANOVA
  • Correlation
  • Linear regression
  • Contingency tables

Read more at the APS observer.

BayesFactorExtras: a sneak preview

Felix Schönbrodt and I have been working on an R package called BayesFactorExtras. This package is designed to work with the BayesFactor package, providing features beyond the core BayesFactor functionality. Currently in the package are:

  1. Sequential Bayes factor plots for visualization of how the Bayes factor changes as data come in: seqBFplot()
  2. Ability to embed R objects directly into HTML reports for reproducible, sharable science:  createDownloadURI()
  3. Interactive BayesFactor objects in HTML reports;  just print the object in a knitr document.
  4. Interactive MCMC objects in HTML reports; just print the object in a knitr document.
All of these are pretty neat, but I thought I’d give a sneak preview of #4. To see how it works, click here to play with the document on Rpubs!

I anticipate releasing this to CRAN soon.